[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Amazon jobs](https://jobs.opennash.com/companies/amazon/) · [Search jobs](/search/) Amazon · Palo Alto, CA # Principal Applied Scientist, Ads Optimization, Full Funnel Agentic Intelligence and Models US, CA, Palo Alto Senior individual contributor Research & Science Listed on Amazon’s official careers site. Posted August 6, 2026. Check that it is still open and apply there. [Apply on Amazon’s careers site →](https://www.amazon.jobs/en/jobs/10494784/principal-applied-scientist-ads-optimization-full-funnel-agentic-intelligence-and-models) [OpenNash Jobs](https://jobs.opennash.com/) / [Amazon](https://jobs.opennash.com/companies/amazon/) / Principal Applied Scientist, Ads Optimization, Full Funnel Agentic Intelligence and Models ## Job description As a Principal Applied Scientist for the Full-Funnel Agentic Campaign optimization team, you will invent the models that jointly allocate budget across sponsored ad products to maximize advertiser outcomes and long-term customer value. This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. - Own the science vision for full-funnel campaign optimization end to end - Invent models for joint budget allocation, incrementality, and long-term value - Take innovations from prototype to production serving live advertiser campaigns - Raise the technical bar across scientists and engineers ### Key job responsibilities - Define the long-term scientific vision for full-funnel campaign optimization, translating ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues. ### A day in the life You move between deep technical work and org-wide influence. A morning might be spent deriving a budget-allocation formulation with two scientists, then reviewing an incrementality experiment design over an advertiser segment. Afternoons bring roadmap alignment with product and engineering partners, a design review that raises the bar on a teammate's model, and a working session on taking a prototype to production. Your customers are Amazon advertisers and their shoppers; your stakeholders span applied science, engineering, and product leadership across the Ads full-funnel organization. ### About the team We build the optimization science behind full-funnel agentic advertising on Amazon: how campaigns are recommended, and how budget is allocated across sponsored ad products to grow advertiser outcomes and long-term customer value. Our mission is to make full-funnel advertising work automatically and measurably for every advertiser, from foundational research through production systems serving live campaigns. We are a science-driven, high-ownership team that values rigorous experimentation, invention where no proven approach exists yet, and close partnership with engineering and product. ## At a glance - Posted by the employer: Aug 6, 2026 - First seen by OpenNash: Aug 20, 2026 - Last checked: Oct 11, 2026 - Listed for: 66 days - Location: US, CA, Palo Alto[Apply on Amazon’s careers site →](https://www.amazon.jobs/en/jobs/10494784/principal-applied-scientist-ads-optimization-full-funnel-agentic-intelligence-and-models) ## More jobs at Amazon ### [Senior Applied Scientist, Sponsored Products and Brands Ads Response Prediction](https://jobs.opennash.com/companies/amazon/jobs/fd6cfb3873e3728c54a4/) US, CA, Palo Alto ### [Senior Applied Scientist, Sponsored Products Bidding](https://jobs.opennash.com/companies/amazon/jobs/0f8da65f3525f203c3be/) US, CA, Palo Alto ### [Machine Learning Engineer, Amazon Ads Identity](https://jobs.opennash.com/companies/amazon/jobs/1bd949a43f9e6cc4698f/) US, CA, Palo Alto ### [Principal PMT, APM Insights](https://jobs.opennash.com/companies/amazon/jobs/3128f32f121a390c57b5/) US, CA, Palo Alto ### [Principal Product Manager - Tech, Sponsored Products and Brands](https://jobs.opennash.com/companies/amazon/jobs/f0731f8baca06fafc7da/) US, CA, Palo Alto ## Similar jobs at other companies ### [Principal Applied Scientist-Ads Monetization](https://jobs.opennash.com/companies/microsoft/jobs/1ed023bd5f77158ca20e/) Microsoft · United States, Washington, Redmond, California, Mountain View ### [Senior Applied Scientist - Ads Monetization](https://jobs.opennash.com/companies/microsoft/jobs/71baa9251ca248c59d91/) Microsoft · United States, Washington, Redmond, California, Mountain View ### [Principal, Data Scientist - Applied Machine Learning](https://jobs.opennash.com/companies/walmart/jobs/8af78490b17976e76747/) Walmart · United States ### [Senior Applied Scientist](https://jobs.opennash.com/companies/oracle/jobs/f8ed9ff14b05042cc65f/) Oracle · United States ### [Senior AI/ML Applied Scientist](https://jobs.opennash.com/companies/unitedhealth-group/jobs/78af7d0e20840c30720f/) UnitedHealth Group · Eden Prairie, Minnesota [Jobs in Palo Alto, CA](/locations/us/ca/palo-alto/)[Jobs in California](/locations/us-ca/)[Research & Science jobs](/functions/research-science/) OpenNash lists jobs from employers’ own careers sites and does not handle applications. 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